Data Engineer

Mumbai, Mumbai, India
Full Time
Experienced

Job Description – Data Engineer

Employment Type: Full-Time
Work Location: KSA
Experience Required: Hands-on experience with Spark development, Informatica integration solutions, and enterprise data platforms.
Years of Experience: 5 to 10

Role Overview

We are looking for a Data Engineer to build, configure, and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform. The ideal candidate will have strong hands-on experience with Spark-based development, enterprise data lake/lakehouse implementations, and Informatica-based integration solutions.


Key Responsibilities

  • Design and develop scalable data ingestion and transformation pipelines.
  • Build, optimize, and maintain Spark-based ETL/ELT workloads.
  • Develop and manage data models and datasets using Apache Iceberg tables.
  • Implement and support data integration workflows using Informatica.
  • Enable analytics and data consumption through Denodo, Starburst, and/or Dremio.
  • Develop secure, governed, and scalable data pipelines aligned with enterprise standards.
  • Support batch and near real-time data processing requirements.
  • Implement data quality, metadata management, and validation controls.
  • Collaborate with Platform Engineers on deployment, integration, and operationalization activities.
  • Support migration, testing, and production rollout initiatives.
  • Troubleshoot and optimize data pipeline performance, reliability, and scalability.

Must-Have Skills

  • Apache Spark (PySpark and/or Scala Spark)
  • Apache Iceberg
  • Informatica Data Integration
  • MinIO Object Storage
  • Denodo
  • SQL and Data Modeling
  • Data Warehousing Concepts
  • ETL/ELT Development
  • Git Version Control

Good-to-Have Skills

  • Starburst and/or Dremio
  • Data Governance and Metadata Management
  • Data Quality Frameworks
  • CI/CD for Data Pipelines
  • Python
  • Kafka or Streaming Technologies
  • Lakehouse Architecture Patterns
  • Experience with enterprise data lake and lakehouse implementations
  • Performance tuning and optimization of large-scale data workloads
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